In communication networks using bandwidth reservations for transmitting data, there is the desire to provide an optimal bandwidth reservation scheme. Such an optimal scheme reserves just sufficient bandwidth for transmission and at a proper time, i.e., the required bandwidth is available to the node in the communication network for transmissions when needed without additional delay. Especially in TDMA networks (TDMA=Time Division Multiple Access), there is a need for an online estimation of bandwidth requirements during run-time of the network. This is because control mechanisms required to reserve bandwidth and compute conflict free transmission schedules involves some inherent latency. The prediction of the bandwidth demand in the future is challenging as complex traffic patterns with quickly changing load have to be taken into account. As the reservation process of new bandwidth is time constrained, it is essential to have a good view on the development of the bandwidth demand in the near future.
The problem of bandwidth requirement estimation and more generally traffic prediction has been studied in many different fields of technologies, such as communication networks, public transportation, electrical and hydraulic networks. In document [1], the so-called ARIMA model (ARIMA=Auto Regressive Integrated Moving Average) is used to predict the bandwidth requirement on a backbone. In this model, the time series is derived until it becomes stationary, then weight coefficients are computed and the forecast uses the weighted sum of the past moving averages. In document [2], an ARCH model (ARCH=Auto Regressive Conditional Heteroskedasticity) is used to forecast bandwidth and help data providers to adjust their bandwidth allocation. This method is based on a quantized approach to allocate and deallocate a bandwidth quantum with different policies.
In document [3], a neural network approach is described to predict needed bandwidth for transmission of a video flow. The publication [4] describes a method to forecast web traffic based on time series of averaged web session measurements. In this document, a wavelet decomposition is coupled with neural networks for prediction. The method in this document does not describe a bandwidth reservation mechanism for data transmission between two nodes in a communication network. Document [5] discloses the use of wavelet filtering to decompose a signal of multiple scales in order to ease information extraction for detecting network traffic anomalies.